April 27, 2017

Why Should Retailers Use a Predictive Model For New Site Search?

Retailers have to make a site selection in order to grow their business. With so many available properties and so many factors to consider, how should one go about selecting the best locations?

Initially, you need to keep your main site goals in mind. Wherever you invest, your site should:

provide the greatest return on investment

maximize market coverage

limit the impact of cannibalization on your existing network of stores

When trying to identify the best properties for new convenience stores, there is an enormous amount of data to assimilate. You will need to understand the competitive environment, (including details about competitors and the strengths and weaknesses of their offerings) as well as demand in the marketplace. How many consumers are living and working in the area? How many are in transit — running errands, going to school and going shopping? There are any number of factors in site health that drive a consumer’s purchase decision, including fuel price, convenience of the location, ancillary offerings and hours open. Successful retailers must understand the intricate details of every potential location before introducing new stores to the market.

Predictive site selection models can help. When considering a new location for a convenience store, it’s important that you assess how a store will perform in the future and how the performance will impact other stores in your current network. There are many reasons predictive models help, but the following two are primary:

1. Subjective approaches can be tempered into Objective decisions.

A model converts the perceived reality (subjective approach) into one that is factual (objective approach), removing the internal biases that can come from personal feelings.

2. Art and experience can be forged into a Science.

Imagine all the different factors that influence the performance of your c-store: large format versus small format, food service offering, the number of cars driving past the site, population, average household income, area businesses, competitor make-up, competitor offerings and more. Models have the ability to understand and quantify all of these factors to consistently make fact-based predictions of future performance.

A predictive model allows you to analyze the outcome of making changes to your network before you actually spend any capital, ensuring a maximum return on investment when you finally do decide to purchase. Models provide both a detailed understanding of the key drivers of the business and precise projections of future performance. When site selection is done right, store networks can be expanded so that return on investment is maximized, cannibalization is minimized and market share is increased.

A model that can scientifically analyze this data to project performance is the way toward an objective view of your future success. With a location model based on proven scientific methods backing your process, you can identify the best opportunities to maximize available capital and position your network for long-term growth — and you will have recruited both your creative and experiential strengths to get there.

Predictive models can also assist retailers with zoning issues that might crop up when looking for new property. A predictive model can help prove that there is consumer demand for a store at a particular location. How? By forecasting the gasoline and convenience store sales for a vacant property along with the volumetric impact the new location will have on nearby competitors. A predictive model can provide proof that there is a need for a new outlet at the location and that it will not be detrimental to the surrounding competitors.

Sophisticated models can also simulate a variety of “what if” scenarios to help you prove how certain restrictions could impact the business. For instance, if only allowing one curb cut versus two would limit accessibility, resulting in decreased sales, you'd need to know that before making a decision about curb cuts. Another example: Restrictions such as forced setbacks causing a reduction in useable space on the property can be simulated with models to show the impact on sales “if” the setback “is” and “is not” implemented in order to prove loss of business that will be incurred.

Seeing the potential impact of a restriction before you implement it or before you purchase a site where it's already implemented means you can make more informed decisions that help you track back to the three main goals of your site search: finding a location that allows you to provide the greatest return on investment, maximize market coverage and limit cannibalization on your existing stores.